test.py

import os
import sys
import glob
import numpy as np
import torch
import utils
import logging
import argparse
import torch.nn as nn
import genotypes
import torch.utils
import torchvision.datasets as dset
import torch.backends.cudnn as cudnn

from model import NetworkCIFAR as Network

parser = argparse.ArgumentParser("cifar")
parser.add_argument('--data', type=str, default='./data', help='location of the data corpus')
parser.add_argument('--batch_size', type=int, default=96, help='batch size')
parser.add_argument('--report_freq', type=float, default=50, help='report frequency')
parser.add_argument('--gpus', type=str, default='0,1', help='gpu device id')
parser.add_argument('--init_channels', type=int, default=36, help='num of init channels')
parser.add_argument('--layers', type=int, default=20, help='total number of layers')
parser.add_argument('--model_path', type=str, default='./trained_model/cifar10_model.pt',
                    help='path of pretrained model')
parser.add_argument('--auxiliary', action='store_true', default=False, help='use auxiliary tower')
parser.add_argument('--cutout', action='store_true', default=False, help='use cutout')
parser.add_argument('--cutout_length', type=int, default=16, help='cutout length')
parser.add_argument('--drop_path_prob', type=float, default=0.2, help='drop path probability')
parser.add_argument('--seed', type=int, default=0, help='random seed')
parser.add_argument('--arch', type=str, default='RelativeNAS', help='which architecture to use')
parser.add_argument('--set', type=str, default="cifar10", help='data set')

args = parser.parse_args()

os.environ['CUDA_VISIBLE_DEVICES'] = '{}'.format(args.gpus)

log_format = '%(asctime)s %(message)s'
logging.basicConfig(stream=sys.stdout, level=logging.INFO,
                    format=log_format, datefmt='%m/%d %I:%M:%S %p')

CIFAR_CLASSES = 10
if args.set == 'cifar100':
    CIFAR_CLASSES = 100


def main():
    if not torch.cuda.is_available():
        logging.info('no gpu device available')
        sys.exit(1)

    np.random.seed(args.seed)
    cudnn.benchmark = True
    torch.manual_seed(args.seed)
    cudnn.enabled = True
    torch.cuda.manual_seed(args.seed)
    logging.info('gpu device = %s' % args.gpus)
    logging.info("args = %s", args)

    genotype = eval("genotypes.%s" % args.arch)
    model = Network(args.init_channels, CIFAR_CLASSES, args.layers, args.auxiliary, genotype)
    model = torch.nn.DataParallel(model)
    model = model.cuda()
    utils.load(model, args.model_path)

    logging.info("param size = %fMB", utils.count_parameters_in_MB(model))

    criterion = nn.CrossEntropyLoss()
    criterion = criterion.cuda()

    if args.set == 'cifar100':
        _, test_transform = utils._data_transforms_cifar100(args)
        test_data = dset.CIFAR100(root=args.data, train=False, download=False, transform=test_transform)

    else:
        _, test_transform = utils._data_transforms_cifar10(args)
        test_data = dset.CIFAR10(root=args.data, train=False, download=False, transform=test_transform)

    test_queue = torch.utils.data.DataLoader(
        test_data, batch_size=args.batch_size, shuffle=False, pin_memory=False, num_workers=2)

    model.module.drop_path_prob = args.drop_path_prob
    test_acc, test_obj = infer(test_queue, model, criterion)
    logging.info('test_acc %f', test_acc)


def infer(test_queue, model, criterion):
    objs = utils.AvgrageMeter()
    top1 = utils.AvgrageMeter()
    top5 = utils.AvgrageMeter()
    model.eval()

    for step, (input, target) in enumerate(test_queue):
        input = input.cuda(non_blocking=True)
        target = target.cuda(non_blocking=True)

        logits, _ = model(input)
        loss = criterion(logits, target)

        prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
        n = input.size(0)
        objs.update(loss.item(), n)
        top1.update(prec1.item(), n)
        top5.update(prec5.item(), n)

        if step % args.report_freq == 0:
            logging.info('test %03d %e %f %f', step, objs.avg, top1.avg, top5.avg)

    return top1.avg, objs.avg


if __name__ == '__main__':
    main()